most citedHigh-Fidelity Image Inpainting with Multimodal Guided GAN Inversion

1 citations · 1 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV2025

PlanarTrack: A high-quality and challenging benchmark for large-scale planar object tracking

Yifan Jiao, Xinran Liu, Xiaoqiong Liu +3

Planar tracking has drawn increasing interest owing to its key roles in robotics and augmented reality. Despite recent great advancement, further development of planar tracking, pa…

cs.CV2025

G3CN: Gaussian Topology Refinement Gated Graph Convolutional Network for Skeleton-Based Action Recognition

Haiqing Ren, Zhongkai Luo, Heng Fan +3

Graph Convolutional Networks (GCNs) have proven to be highly effective for skeleton-based action recognition, primarily due to their ability to leverage graph topology for feature…

cs.CV2025

CGTrack: Cascade Gating Network with Hierarchical Feature Aggregation for UAV Tracking

Weihong Li, Xiaoqiong Liu, Heng Fan +1

Recent advancements in visual object tracking have markedly improved the capabilities of unmanned aerial vehicle (UAV) tracking, which is a critical component in real-world robotic…

cs.CV20251 cited

High-Fidelity Image Inpainting with Multimodal Guided GAN Inversion

Libo Zhang, Yongsheng Yu, Jiali Yao +1

Generative Adversarial Network (GAN) inversion have demonstrated excellent performance in image inpainting that aims to restore lost or damaged image texture using its unmasked con…

cs.CV2025

OmniSTVG: Toward Spatio-Temporal Omni-Object Video Grounding

Jiali Yao, Xinran Deng, Xin Gu +6

In this paper, we propose spatio-temporal omni-object video grounding, dubbed OmniSTVG, a new STVG task that aims at localizing spatially and temporally all targets mentioned in th…

cs.CV2025

Attention to Trajectory: Trajectory-Aware Open-Vocabulary Tracking

Yunhao Li, Yifan Jiao, Dan Meng +2

Open-Vocabulary Multi-Object Tracking (OV-MOT) aims to enable approaches to track objects without being limited to a predefined set of categories. Current OV-MOT methods typically…